This invention relates generally to methods for predicting risk of medical conditions. More particularly, it relates to methods for predicting risk of serious gastrointestinal complications in patients taking nonsteroidal anti-inflammatory drugs.
Gastrointestinal (GI) complications related to nonsteroidal anti-inflammatory drug (NSAID) therapy are the most prevalent category of adverse drug reactions in the United States. The most serious and life-threatening complications include ulcers or bleeding, and require immediate hospitalization. The risk of GI hospitalization has been estimated at 1% to 1.5% in people taking NSAIDs, and the risk of death at 0.13%. In 1997, about 16,500 Americans died from bleeding stomach ulcers caused by NSAIDs. Patients with arthritis are among the most frequent users of NSAIDs and are therefore particularly at risk for these side effects. Often, there are no previous symptoms before a patient is hospitalized.
Some patients taking NSAIDs, for example older patients, are believed to be at a much higher risk for GI side effects than others. There is currently no way to accurately determine individual risk, however, even qualitatively. Physicians prescribing NSAIDs to arthritis patients cannot assess the treatment""s safety until a side effect occurs. While some risk factors are believed to be important, physicians can currently base drug therapy recommendations only on their intuitive, subjective clinical judgment. In addition, as managed care becomes the health care standard, patients are becoming more informed about their personal health and less trusting of health care providers, and would like to be able to assess their own risk.
Individual risk factors have been suggested in the literature, but it is difficult to estimate an overall patient risk from these various risk factors. Some of the individual risk factors include age, NSAID therapy duration, and use of H2 antagonists, antacids, corticosteroids, or anticoagulants. A multivariate risk factor model for estimation of risk in an individual patient is described in J. F. Fries et al., xe2x80x9cNonsteroidal Anti-Inflammatory Drug-Associated Gastropathy: Incidence and Risk Factor Models,xe2x80x9d American Journal of Medicine, Vol. 91, pp. 213-222 (1991). The model is a classification tree that provides an estimated risk for each branch of the tree. Different values of predictive factors, for example, age and disease duration, cause branching in one of a number of directions. While it does include a variety of risk factors, the model and its associated GI event score table are cumbersome to use and cannot easily be extended to multiple years. The stepwise logistic regression methods used to develop the model are also not the most accurate for the analysis of longitudinal data, because they are not time oriented. That is, they do not take into account the time to the event or the time of observation for the patients who do not have an event. In addition, the calculation must be done by a health care provider, and is not suited for use by a patient.
There is clearly still a need for a simple and accurate method for determining a patient""s risk of a serious GI hospitalization while taking NSAIDs. Once this risk is known, a physician can make appropriate drug therapy recommendations.
Accordingly, it is an object of the present invention to provide a simple method for determining the estimated risk of GI hospitalization of a patient taking NSAIDs that:
1) includes many significant variables to calculate an overall risk;
2) is highly accurate;
3) can easily be extended to multiple years;
4) uses data that is easily obtained from a patient;
5) provides a simple additive point system; and
6) can be used by patients without help from a physician.
These objects and advantages are attained by a computer-implemented method for determining the estimated risk R of serious gastrointestinal hospitalization of a patient taking nonsteroidal anti-inflammatory drugs (NSAIDs). A physician uses the estimated risk R to determine whether an appropriate drug therapy includes NSAIDs.
The method includes three main steps: obtaining patient values of a plurality of predictive factors x1, . . . , xn; calculating the estimated risk R from a model applied to the patient values; and displaying the calculated estimated risk R. The predictive factors may include age, baseline global health status, proportion of time taking prednisone, previous occurrence of a GI side effect, and previous occurrence of GI hospitalization.
The model is preferably a Cox proportional hazard model derived from a patient database. In one main embodiment, the estimated risk R=1xe2x88x92S(1)B, where S(1) is a probability of not having a serious GI hospitalization within one year, B=eA, A=Lxe2x88x92M,       M    =                  ∑                  i          =          1                n            ⁢              xe2x80x83            ⁢                        a          i                ⁢                  m          i                      ,      xe2x80x83    ⁢      L    =                  ∑                  i          =          1                n            ⁢              xe2x80x83            ⁢                        a          i                ⁢                  x          i                      ,
mi are predetermined mean values of the predictive factors, and ai are Cox coefficients. Specifically, for n=5 and risk factors listed above, S(1)=0.99045, the predetermined mean values are m1=56.82 years, m2=41.04, m3=0.374, m4=0.263, and m5=0.013, and the Cox coefficients are a1=0.050/years, a2=0.010, a3=1.109, a4=0.373, and a5=1.957.
Alternately, the estimated risk can be calculated from a point value method. First, a point value pi is assigned to each predictive factor, where the point value is related to the patient value of the predictive factor. For example, an age between 61 and 65 receives a point value of 9. The point values may instead be obtained directly from the patient in the first step of the method. Next, the point values for each predictive factor are added to produce a point total P. Finally, the point total P is converted to an estimated risk R. In one embodiment of a Cox point-based categorical method for n=5, the estimated risk R=1xe2x88x92S(1)B, where S(1)=0.99049, B=eA, A=Lxe2x88x92M, M=2.7662, and L=0.237*P.